A novel multilayer AAA model for integrated applications

Neural Computing and Applications - Nowadays, one of the problems in current authentication, authorization and accounting (AAA) model is lack of accurate roadmap of access management in integrated...     

Prediction of heavy oil viscosity using a radial basis function neural network

Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data.     

Constraint-wrench analysis of robotic manipulators

The constraint-wrench analysis of mechanisms, with focus on parallel robots, is the subject of this paper. Although the method proposed here can be generalized for parallel robots with multiple-loop kinematic chains, here, single-loop chains are targeted. To this end, a novel representation of the constraints imposed by the kinematic pairs is introduced. With this representation, the constraint matrix of a mechanism is readily derived. For the calculation of the constraint wrenches, by means of the constraint matrix and based on the Newton–Euler formulation, a new procedure is introduced. As a case study, the constraint wrench analysis of the McGill Schönflies Motion Generator (SMG), while undergoing a test cycle adopted by the industry, is conducted.     

Nicotinic Acid Increases Adiponectin Secretion from Differentiated Bovine Preadipocytes through G-Protein Coupled Receptor Signaling

The transition period in dairy cows (3 weeks prepartum until 3 weeks postpartum) is associated with substantial mobilization of energy stores, which is often associated with metabolic diseases. Nicotinic acid (NA) is an antilipolytic and lipid-lowering compound used to treat dyslipidaemia in humans, and it also reduces non-esterified fatty acids in cattle. In mice the G-protein coupled receptor 109A (GPR109A) ligand NA positively affects the secretion of adiponectin, an important modulator of glucose and fat metabolism. In cattle, the corresponding data linking NA to adiponectin are missing. Our objective was to examine the effects of NA on adiponectin and AMPK protein abundance and the expression of mRNAs of related genes such as chemerin, an adipokine that enhances adiponectin secretion in vitro. Differentiated bovine adipocytes were incubated with pertussis toxin (PTX) to verify the involvement of GPR signaling, and treated with 10 or 15 µM NA for 12 or 24 h. NA increased adiponectin concentrations (p ≤ 0.001) and the mRNA abundances of GPR109A (p ≤ 0.05) and chemerin (p ≤ 0.01). Pre-incubation with PTX reduced the adiponectin response to NA (p ≤ 0.001). The NA-stimulated secretion of adiponectin and the mRNA expression of chemerin in the bovine adipocytes were suggestive of GPR signaling-dependent improved insulin sensitivity and/or adipocyte metabolism in dairy cows.     

大功率、可扩展、封装占板面积很小、产生热量更少的POL稳压器

3D封装方法允许大功率POL模块型稳压器占用很小的PCB面积,允许高效率散热。介绍了通过3D封装架构和灵活的组件放置方式解决热量问题的μModule稳压器LTM4636系列的性能特点。     

Using Fuzzy Cost‐Based FMEA,GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service

This paper proposes an integrated approach to identify, evaluate and improve the potential failures in a service setting. This integrated approach combines Fuzzy cost‐based service‐specific FMEA (FCS‐FMEA), Grey Relational Analysis (GRA) and profitability theory for better prioritization of the service failures by considering cost as an important issue and using the profitability theory in a way that the corrective actions costs are taken into account. Considering profitability with FCS‐FMEA and GRA reduces the losses caused by failure occurrence. Besides, a maximization linear mathematical problem is used to select the best mix of failures to be repaired. We apply our approach to an academic example concerning the potential failures diagnosis of the Internal Medicine service of a hospital located in Seoul, Korea. We applied our approach and solved the associated maximization problem by a commercial solver, producing an optimal solution which indicates the most convenient mix of failures to be repaired by considering available budget. Copyright © 2013 John Wiley & Sons, Ltd.     

An energy‐efficient utility‐based distributed data routing scheme for heterogenous sensor networks

A utility‐based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game‐theoretic heuristic load‐balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy‐efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load‐balanced trees, resulting in over 30% longer network lifetime compared with the cumulative algorithm proposed in the literature. Copyright © 2014 John Wiley & Sons, Ltd.     

Application of mesoporous magnetic carbon composite for reactive dyes removal: Process optimization using response surface methodology

Discharging the effluents of textile wastewaters into potable water resources can endanger the ecosystem, due to their reactivity, toxicity, and chemical stability. In this research, the application of powder activated carbon modified with magnetite nanoparticles (PAC-MNPs) as an adsorbent for removal of reactive dyes (Reactive black 5 (RB5) and reactive red 120 (RR120)) was studied in a batch system. The adsorption performance was evaluated as a function of temperature, contact time and different adsorbent and adsorbate concentrations. The levels of factors were statistically optimized using Box-Behnken Design (BBD) from the response surface methodology (RSM) to maximize the efficiency of the system. The adsorption process of both dyes was fit with the pseudo-second order kinetic and Langmuir isotherm models. The identified optimum conditions of adsorption were 38.7 °C, 46.3 min, 0.8 g/L and 102 mg/L for temperature, contact time, adsorbent dose, and initial dyes concentration, respectively. According to the Langmuir isotherm, the maximum sorption capacities of 175.4 and 172.4 mg/g were obtained for RB5 and RR120, respectively. Thermodynamics studies indicated that the adsorption process of the reactive dyes was spontaneous, feasible, and endothermic. After five cycles, the adsorption efficiency was around 84 and 83% for RB5 and RR120, respectively. A high value of desorption was achieved, suggesting that the PAC-MNPs have a good potential in regeneration and reusability, and also can be effectively utilized in industrial applications. PAC-MNPs also show a good anti-interference potential for removal of reactive dyes in dye-industry wastewaters.